Sampling Modality Influences the Predictive Value of Grading in Adult Soft Tissue Extremity Sarcomas
Bibliographic record
Abstract
CONTEXT: Histologic grade is one of the best predictors of outcome in adult soft tissue sarcomas. Current grading systems were validated on resection specimens; however, there has been a trend toward the use of biopsies to diagnosis these tumors. OBJECTIVES: To determine whether the grade of an extremity soft tissue sarcoma determined on tissue obtained by either core needle biopsy or incisional biopsy is predictive of metastasis- or disease-free survival, and whether either sampling modality is superior. DESIGN: One hundred three core needle biopsies and 107 incisional biopsies of nonmetastatic spindle cell sarcomas of the extremities were retrieved from the archives. All cases had a minimum 2-year follow-up. Patient data and outcome and tumor characteristics were recorded. Tumors were reviewed and evaluated using the French Federation of Cancer Centers Sarcoma Group grading system. Kaplan-Meier survival curves were generated to correlate tumor grade with metastasis- and disease-free survival for both groups. RESULTS: Patient and tumor characteristics were similar between groups except that more tumors were grade 3 and superficial in the incisional biopsy group. Grade determined on core needle biopsy was not predictive of either metastasis-free survival (P = .59) or disease-free survival (P = .50). In contrast, grade determined on incisional biopsy was predictive of both metastasis-free survival (P < .001) and disease-free survival (P = .001). CONCLUSIONS: Biopsy, particularly core needle biopsy, represents a convenient diagnostic tool, particularly in the context of neoadjuvant therapy. However, based on these results incisional biopsy is recommended if grading is to be used to predict prognosis in spindle cell soft tissue sarcomas of the extremities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".